Abstract

Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence.

Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. These operations include registration across imaging cycles (a critical step in multiplexed image analysis), deconvolution, point spread function (PSF) generation, image quality assessment, and cell/nuclei segmentation. The remaining tools offered facilitate visualization, image subset extraction with ImageJ compatibility, and iterative pipeline optimization.